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CRII: CHS: RUI: Computational models of humans for studying and improving Human-AI interaction

CRII：CHS：RUI：用于研究和改善人机交互的人类计算模型

基本信息

批准号：
2218226
负责人：
Christopher Dancy
金额：
$ 17.41万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-15 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218226&HistoricalAwards=false
关键词：
CRII CHS RUI Computational models

项目摘要

Understanding the interactions between humans and systems utilizing artificial intelligence (AI) requires an understanding of how human physiological changes impact memory and other mental processes. To realize the beneficial societal outcomes on, for example, interactions between humans and intelligent robots, it is important to develop simulations to test a variety of situations where memory under arousal states will function. In this project, the investigator articulates a research plan to use a simulation of the human mind and body to understand effects of physiological arousal on human memory and cognition and the consequences for human interaction with AI agents. Human-subject studies will be used to introduce stimuli to induce human arousal by using selected stressors and collect physiological and behavioral data during tasks that require cooperation between humans and AI agents. Societal benefits include an architecture to simulate a variety of human-AI interactions under various levels of arousal and stress. This architecture can be used to explore new ways to co-team humans with AI agents and set expectations for positive and/or negative behaviors that might occur in such collaborations. Undergraduate students at Bucknell University will be heavily involved as research assistants on this project. The investigator plans to develop simulations and elicit arousal states in human operators performing collaborative tasks with agents enabled by artificial intelligence (AI) to understand human-AI interaction. The goal is to better understand, through simulation, how algorithms for intelligent agents can be advanced and expanded to respond to human variations in behavior and memory processes under different levels of arousal, including levels that mimic stress. The objectives include developing simulations to examine contexts and tasks; understanding how environmental stimuli affect interactions between humans and intelligent agents; and determining how to advance algorithms to optimize human-AI cooperation and avoid maladaptive interactions. The investigator has already extended Adaptive Control of Thought-Rational (ACT-R) architecture to account for physiological influences on declarative and procedural memory. Physio-cognitive agents will be developed based on Bayesian and reinforcement learning to acquire knowledge of their environment. Human-AI task simulations will be implemented in a virtual environment to understand how arousal mediates intelligent behavior and how interaction with external environments that include AI agents may change performance, including through maladaptive behavior. The project seeks to discover computationally-enabled processes and contexts to amplify human capabilities. The resulting revised models and open-source code will be made available to the public for further explorations in human-AI interaction. The university is an undergraduate institution allowing significant participation of undergraduates in research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

要了解利用人工智能(AI)的人与系统之间的相互作用，需要了解人类生理变化如何影响记忆和其他心理过程。为了实现有益的社会结果，例如，在人类和智能机器人之间的互动中，重要的是开发模拟来测试在唤醒状态下记忆发挥作用的各种情况。在这个项目中，研究人员阐述了一项研究计划，使用对人类身心的模拟来了解生理唤醒对人类记忆和认知的影响，以及人类与人工智能代理交互的后果。人体-受试者研究将被用来引入刺激，通过使用选定的应激源来诱导人类唤醒，并在需要人类和人工智能代理合作的任务期间收集生理和行为数据。社会效益包括一种架构，可以模拟在不同水平的唤醒和压力下的各种人类-人工智能互动。这种架构可以用来探索将人类与人工智能代理合作的新方法，并设置对此类协作中可能出现的积极和/或消极行为的预期。巴克内尔大学的本科生将作为研究助理参与到这个项目中来。这位研究人员计划开发模拟，并在人类操作员与人工智能(AI)支持的代理执行协作任务时引发唤醒状态，以了解人类与AI的交互。其目标是通过模拟更好地理解智能代理的算法如何改进和扩展，以应对不同唤醒水平下人类行为和记忆过程的变化，包括模拟压力的水平。目标包括开发模拟以检查上下文和任务；了解环境刺激如何影响人类与智能代理之间的交互；以及确定如何改进算法以优化人类与人工智能的合作并避免适应不良的交互。研究人员已经扩展了思维理性的自适应控制(ACT-R)架构，以解释对陈述性和程序性记忆的生理影响。将基于贝叶斯和强化学习开发物理认知代理，以获取其环境的知识。人类-AI任务模拟将在虚拟环境中实施，以了解唤醒如何调节智能行为，以及与包括AI代理在内的外部环境的交互可能如何改变性能，包括通过适应不良行为。该项目寻求发现支持计算的过程和上下文，以放大人类的能力。由此产生的修订后的模型和开放源代码将向公众开放，供进一步探索人类与人工智能的交互。该大学是一所本科院校，允许本科生大量参与研究。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。